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406 lines
21 KiB
Python
406 lines
21 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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'''
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Adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py
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Original author Wei Wu
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Implemented the following paper:
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Identity Mappings in Deep Residual Networks"
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'''
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import mxnet as mx
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import numpy as np
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def Conv(**kwargs):
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#name = kwargs.get('name')
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#_weight = mx.symbol.Variable(name+'_weight')
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#_bias = mx.symbol.Variable(name+'_bias', lr_mult=2.0, wd_mult=0.0)
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#body = mx.sym.Convolution(weight = _weight, bias = _bias, **kwargs)
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body = mx.sym.Convolution(**kwargs)
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return body
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def Act(data, act_type, name):
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#ignore param act_type, set it in this function
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body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name)
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return body
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def residual_unit_v1(data, num_filter, stride, dim_match, name, bottle_neck=True, use_se=False, bn_mom=0.9, workspace=256, memonger=False):
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"""Return ResNet Unit symbol for building ResNet
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Parameters
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----------
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data : str
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Input data
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num_filter : int
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Number of output channels
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bnf : int
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Bottle neck channels factor with regard to num_filter
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stride : tuple
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Stride used in convolution
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dim_match : Boolean
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True means channel number between input and output is the same, otherwise means differ
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name : str
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Base name of the operators
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workspace : int
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Workspace used in convolution operator
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"""
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if bottle_neck:
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conv1 = Conv(data=data, num_filter=int(num_filter*0.25), kernel=(1,1), stride=stride, pad=(0,0),
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no_bias=True, workspace=workspace, name=name + '_conv1')
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bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
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act1 = Act(data=bn1, act_type='relu', name=name + '_relu1')
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conv2 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv2')
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bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
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act2 = Act(data=bn2, act_type='relu', name=name + '_relu2')
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conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True,
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workspace=workspace, name=name + '_conv3')
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bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')
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if use_se:
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#se begin
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body = mx.sym.Pooling(data=bn3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')
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body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv1", workspace=workspace)
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body = Act(data=body, act_type='relu', name=name+'_se_relu1')
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body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv2", workspace=workspace)
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body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid")
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bn3 = mx.symbol.broadcast_mul(bn3, body)
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#se end
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if dim_match:
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shortcut = data
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else:
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conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
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workspace=workspace, name=name+'_conv1sc')
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shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc')
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if memonger:
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shortcut._set_attr(mirror_stage='True')
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return Act(data=bn3 + shortcut, act_type='relu', name=name + '_relu3')
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else:
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conv1 = Conv(data=data, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv1')
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bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')
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act1 = Act(data=bn1, act_type='relu', name=name + '_relu1')
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conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv2')
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bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')
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if use_se:
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#se begin
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body = mx.sym.Pooling(data=bn2, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')
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body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv1", workspace=workspace)
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body = Act(data=body, act_type='relu', name=name+'_se_relu1')
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body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv2", workspace=workspace)
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body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid")
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bn2 = mx.symbol.broadcast_mul(bn2, body)
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#se end
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if dim_match:
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shortcut = data
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else:
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conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
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workspace=workspace, name=name+'_conv1sc')
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shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc')
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if memonger:
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shortcut._set_attr(mirror_stage='True')
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return Act(data=bn2 + shortcut, act_type='relu', name=name + '_relu3')
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def residual_unit_v2(data, num_filter, stride, dim_match, name, bottle_neck=True, use_se=False, bn_mom=0.9, workspace=256, memonger=False):
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"""Return ResNet Unit symbol for building ResNet
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Parameters
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----------
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data : str
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Input data
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num_filter : int
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Number of output channels
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bnf : int
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Bottle neck channels factor with regard to num_filter
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stride : tuple
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Stride used in convolution
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dim_match : Boolean
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True means channel number between input and output is the same, otherwise means differ
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name : str
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Base name of the operators
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workspace : int
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Workspace used in convolution operator
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"""
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if bottle_neck:
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# the same as https://github.com/facebook/fb.resnet.torch#notes, a bit difference with origin paper
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bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
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act1 = Act(data=bn1, act_type='relu', name=name + '_relu1')
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conv1 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0),
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no_bias=True, workspace=workspace, name=name + '_conv1')
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bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
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act2 = Act(data=bn2, act_type='relu', name=name + '_relu2')
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conv2 = Conv(data=act2, num_filter=int(num_filter*0.25), kernel=(3,3), stride=stride, pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv2')
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bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')
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act3 = Act(data=bn3, act_type='relu', name=name + '_relu3')
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conv3 = Conv(data=act3, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True,
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workspace=workspace, name=name + '_conv3')
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if use_se:
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#se begin
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body = mx.sym.Pooling(data=conv3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')
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body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv1", workspace=workspace)
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body = Act(data=body, act_type='relu', name=name+'_se_relu1')
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body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv2", workspace=workspace)
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body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid")
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conv3 = mx.symbol.broadcast_mul(conv3, body)
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if dim_match:
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shortcut = data
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else:
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shortcut = Conv(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
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workspace=workspace, name=name+'_sc')
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if memonger:
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shortcut._set_attr(mirror_stage='True')
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return conv3 + shortcut
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else:
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bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')
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act1 = Act(data=bn1, act_type='relu', name=name + '_relu1')
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conv1 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv1')
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bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')
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act2 = Act(data=bn2, act_type='relu', name=name + '_relu2')
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conv2 = Conv(data=act2, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv2')
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if use_se:
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#se begin
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body = mx.sym.Pooling(data=conv2, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')
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body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv1", workspace=workspace)
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body = Act(data=body, act_type='relu', name=name+'_se_relu1')
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body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv2", workspace=workspace)
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body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid")
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conv2 = mx.symbol.broadcast_mul(conv2, body)
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if dim_match:
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shortcut = data
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else:
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shortcut = Conv(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
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workspace=workspace, name=name+'_sc')
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if memonger:
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shortcut._set_attr(mirror_stage='True')
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return conv2 + shortcut
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def residual_unit_v3(data, num_filter, stride, dim_match, name, bottle_neck=True, use_se=False, bn_mom=0.9, workspace=256, memonger=False):
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"""Return ResNet Unit symbol for building ResNet
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Parameters
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----------
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data : str
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Input data
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num_filter : int
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Number of output channels
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bnf : int
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Bottle neck channels factor with regard to num_filter
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stride : tuple
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Stride used in convolution
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dim_match : Boolean
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True means channel number between input and output is the same, otherwise means differ
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name : str
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Base name of the operators
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workspace : int
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Workspace used in convolution operator
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"""
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if bottle_neck:
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bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
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conv1 = Conv(data=bn1, num_filter=int(num_filter*0.25), kernel=(1,1), stride=stride, pad=(0,0),
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no_bias=True, workspace=workspace, name=name + '_conv1')
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bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
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act1 = Act(data=bn2, act_type='relu', name=name + '_relu1')
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conv2 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv2')
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bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')
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act2 = Act(data=bn3, act_type='relu', name=name + '_relu2')
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conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True,
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workspace=workspace, name=name + '_conv3')
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bn4 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn4')
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if use_se:
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#se begin
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body = mx.sym.Pooling(data=bn4, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')
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body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv1", workspace=workspace)
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body = Act(data=body, act_type='relu', name=name+'_se_relu1')
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body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv2", workspace=workspace)
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body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid")
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bn4 = mx.symbol.broadcast_mul(bn4, body)
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#se end
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if dim_match:
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shortcut = data
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else:
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conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
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workspace=workspace, name=name+'_conv1sc')
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shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc')
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if memonger:
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shortcut._set_attr(mirror_stage='True')
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return bn4 + shortcut
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else:
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bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
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conv1 = Conv(data=bn1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv1')
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bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
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act1 = Act(data=bn2, act_type='relu', name=name + '_relu1')
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conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),
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no_bias=True, workspace=workspace, name=name + '_conv2')
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bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')
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if use_se:
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#se begin
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body = mx.sym.Pooling(data=bn3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')
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body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv1", workspace=workspace)
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body = Act(data=body, act_type='relu', name=name+'_se_relu1')
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body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),
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name=name+"_se_conv2", workspace=workspace)
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body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid")
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bn3 = mx.symbol.broadcast_mul(bn3, body)
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#se end
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if dim_match:
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shortcut = data
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else:
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conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
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workspace=workspace, name=name+'_conv1sc')
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shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc')
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if memonger:
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shortcut._set_attr(mirror_stage='True')
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return bn3 + shortcut
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def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck=True, use_se=False, bn_mom=0.9, workspace=256, memonger=False):
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return residual_unit_v1(data, num_filter, stride, dim_match, name, bottle_neck, use_se, bn_mom, workspace, memonger)
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def resnet(units, num_stages, filter_list, num_classes, bottle_neck=True, use_se=False, bn_mom=0.9, workspace=256, memonger=False):
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"""Return ResNet symbol of
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Parameters
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----------
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units : list
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Number of units in each stage
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num_stages : int
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Number of stage
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filter_list : list
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Channel size of each stage
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num_classes : int
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Ouput size of symbol
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dataset : str
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Dataset type, only cifar10 and imagenet supports
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workspace : int
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Workspace used in convolution operator
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"""
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L_type = False
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fc_type = 'B'#'A'-'E'
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num_unit = len(units)
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assert(num_unit == num_stages)
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data = mx.sym.Variable(name='data')
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data = mx.sym.identity(data=data, name='id')
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data = data-127.5
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data = data*0.0078125
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#data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')
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if not L_type:
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body = Conv(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3),
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no_bias=True, name="conv0", workspace=workspace)
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else:
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body = Conv(data=data, num_filter=filter_list[0], kernel=(3,3), stride=(1,1), pad=(1, 1),
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no_bias=True, name="conv0", workspace=workspace)
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body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')
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body = Act(data=body, act_type='relu', name='relu0')
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body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')
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for i in range(num_stages):
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body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False,
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name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, use_se=use_se,workspace=workspace,
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memonger=memonger)
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|
#body = residual_unit(body, filter_list[i+1], (2, 2), False,
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# name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, use_se=use_se,workspace=workspace,
|
|
# memonger=memonger)
|
|
for j in range(units[i]-1):
|
|
body = residual_unit(body, filter_list[i+1], (1,1), True, name='stage%d_unit%d' % (i+1, j+2),
|
|
bottle_neck=bottle_neck, use_se=use_se, workspace=workspace, memonger=memonger)
|
|
|
|
|
|
if fc_type=='E':
|
|
body = mx.symbol.Dropout(data=body, p=0.4)
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|
fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1')
|
|
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
|
|
else:
|
|
bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')
|
|
relu1 = Act(data=bn1, act_type='relu', name='relu1')
|
|
# Although kernel is not used here when global_pool=True, we should put one
|
|
pool1 = mx.sym.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
|
|
flat = mx.sym.Flatten(data=pool1)
|
|
if fc_type=='A':
|
|
fc1 = flat
|
|
else:
|
|
#B
|
|
fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='pre_fc1')
|
|
if fc_type=='C':
|
|
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
|
|
elif fc_type=='D':
|
|
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
|
|
fc1 = Act(data=fc1, act_type='relu', name='fc1_relu')
|
|
return fc1
|
|
|
|
def get_symbol(num_classes, num_layers, conv_workspace=256, **kwargs):
|
|
"""
|
|
Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py
|
|
Original author Wei Wu
|
|
"""
|
|
if num_layers >= 50:
|
|
filter_list = [64, 256, 512, 1024, 2048]
|
|
bottle_neck = True
|
|
else:
|
|
filter_list = [64, 64, 128, 256, 512]
|
|
bottle_neck = False
|
|
num_stages = 4
|
|
if num_layers == 18:
|
|
units = [2, 2, 2, 2]
|
|
elif num_layers == 34:
|
|
units = [3, 4, 6, 3]
|
|
elif num_layers == 49:
|
|
units = [3, 4, 14, 3]
|
|
elif num_layers == 50:
|
|
units = [3, 4, 6, 3]
|
|
elif num_layers == 101:
|
|
units = [3, 4, 23, 3]
|
|
elif num_layers == 152:
|
|
units = [3, 8, 36, 3]
|
|
elif num_layers == 200:
|
|
units = [3, 24, 36, 3]
|
|
elif num_layers == 269:
|
|
units = [3, 30, 48, 8]
|
|
else:
|
|
raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers))
|
|
|
|
use_se = kwargs.get('use_se', False)
|
|
print('use_se', use_se)
|
|
return resnet(units = units,
|
|
num_stages = num_stages,
|
|
filter_list = filter_list,
|
|
num_classes = num_classes,
|
|
bottle_neck = bottle_neck,
|
|
use_se = use_se,
|
|
workspace = conv_workspace)
|